Paper: A Bayesian Model for Unsupervised Semantic Parsing

ACL ID P11-1145
Title A Bayesian Model for Unsupervised Semantic Parsing
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2011
Authors

We propose a non-parametric Bayesian model for unsupervised semantic parsing. Follow- ing Poon and Domingos (2009), we consider a semantic parsing setting where the goal is to (1) decompose the syntactic dependency tree of a sentence into fragments, (2) assign each of these fragments to a cluster of semanti- cally equivalent syntactic structures, and (3) predict predicate-argument relations between the fragments. We use hierarchical Pitman- Yor processes to model statistical dependen- cies between meaning representations of pred- icates and those of their arguments, as well as the clusters of their syntactic realizations. We develop a modification of the Metropolis- Hastings split-merge sampler, resulting in an efficient inference algorithm for the model. The method is experimentally evalu...